Article Figures & Data
Figures
- FIG 1
A principal-coordinate analysis plot of UniFrac distances from de novo OTUs as visualized by Emperor. A subset of American Gut Project samples spanning sequencing centers and rounds were selected. UCLUST (3) was run independently per round via QIIME. The resulting OTU tables were merged, normalizing sequencing identifiers (IDs) such that if the same sequence was observed in multiple rounds it would receive the same ID. Observations with fewer than 10 counts were dropped. The data were rarefied to 5,000 sequences per sample. The plot shown is based on unweighted UniFrac distances, and the samples are colored by the sequencing center. An interactive visualization can be viewed at https://nbviewer.jupyter.org/github/knightlab-analyses/deblur-manuscript/blob/master/embedded_figure_1.ipynb; the coloring used in the static image can be done by selecting “run_center” as the scatter field. CU, University of Colorado Boulder; ANL, Argonne National Laboratory; UCSD, University of California San Diego.
- FIG 2
Benchmarks of OTU picking tools on artificial communities. (A) A simulation was performed on the basis of samples from a real fecal community (11) using the 52 most abundant bacterial species identified in this study. Reads were then simulated using an ART Illumina (12) read simulator. OTU picking was performed on these simulated reads using UNOISE2, DADA2, and Deblur. The relative abundances predicted by each of these tools and the ground truth (GT) are shown in the heat map. The dendrogram was built using hierarchical clustering based on the Hamming distance between the sequences, with numbers indicating sequence similarity (log scale). (B) Simulated communities with various levels of sequence-sequence similarity. Unweighted UniFrac distances of the predicted OTUs from UNOISE2, DADA2, and Deblur were compared to those of the original composition of the simulated communities. The x axis denotes the similarity radius for each community. The shaded area denotes the standard error of the mean distance estimation (based on 10 random repeats per community). (C) Similar to panel B but with the ratio of observed OTUs (predicted by UNOISE2, DADA2, and Deblur) to actual OTUs in each simulation indicated. (D) Performance of Deblur, UNOISE2, and DADA2 on the even1 community from mock-3 (14). GT data denote the expected ground truth relative frequency for each sOTU as informed by the design of the mock community. Dendrograms and colors are the same as described for panel A.
- FIG 3
Benchmarks of OTU picking tools on natural communities. (A) Stability analysis on experimental technical repeats. Data indicate fractions of overlapping sOTUs from two technical replicates in all OTUs as a function of the minimal frequency threshold present in one of the repeats. (B and C) Application of Deblur in the howler monkey data set. (B) Fraction of sequences matching entries in the NCBI nr/nt database (as of 1 December 2016) with 0.1 or 2 mismatches (red, green, or blue, respectively) from sOTUs unique to Deblur or to DADA2 or present in both (left to right). (C) Heat maps showing sOTUs (rows) in common with Deblur and DADA2, as well as those unique to Deblur and DADA2 (bottom, middle, and top rows, respectively). Samples (columns) are sorted by species and habitat. A total of 200 sOTUs per group (i.e., common, unique to Deblur, or unique to DADA2) were randomly selected for visualization purposes. (D) Single-threaded runtime comparison of Deblur, DADA2, and UNOISE2 against one of the stability MiSeq runs at increasing numbers of samples.
- FIG 4
A principal-coordinate analysis plot of UniFrac distances from Deblur as visualized by Emperor. A subset of American Gut Project samples spanning sequencing centers and rounds were selected. Each sample was processed separately by Deblur. Observations with fewer than 10 counts were dropped. The data were rarefied to 5,000 sequences per sample. The plot shown is based on unweighted UniFrac distances and is colored according to the round of sequencing in the American Gut Project (AG). An interactive visualization can be viewed at https://nbviewer.jupyter.org/github/knightlab-analyses/deblur-manuscript/blob/master/embedded_figure_4.ipynb; the coloring used in the static image can be made by selecting the “center_project_name” as the scatter field.
- FIG 5
A principal-coordinate analysis plot of UniFrac distances from UNOISE2 as visualized by Emperor. A subset of American Gut Project samples spanning sequencing centers and rounds were selected. UNOISE2 was run independently per round. The resulting sOTU tables were merged, normalizing sequencing IDs such that if the same sequence were observed in multiple rounds it would receive the same ID. Observations with fewer than 10 counts were dropped. The data were rarefied to 5,000 sequences per sample. The plot shown is based on unweighted UniFrac distances and is colored according to the round of sequencing in the American Gut Project. An interactive visualization can be viewed at https://nbviewer.jupyter.org/github/knightlab-analyses/deblur-manuscript/blob/master/embedded_figure_5.ipynb; the coloring used in the static image can be made by selecting the “center_project_name” as the scatter field. The static shot is oriented to show PC1 versus PC2, and the separation is more pronounced if orienting the projection to look at PC2 versus PC3.
Supplemental Material
TEXT S1
Details on materials and methods and experimental design. Download TEXT S1, DOCX file, 0.1 MB.
Copyright © 2017 Amir et al.
This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.
FIG S1
The Deblur pipeline. A demultiplexed and quality filtered fasta/fastq file (or a directory of per-sample FASTA/FASTQ files) is used as the input to the pipeline. Following initial splitting to per-sample fasta files, all processing is done independently on each sample. Sequences are trimmed and dereplicated with singletons removed. Reads are then depleted from sequencing artifacts either using a set of known sequencing artifacts (such as PhiX) (negative filtering) or using a set of known 16S sequences (positive filtering). Resulting nonartifact reads are then aligned for easy indel detection. This multiple sequence alignment is then used as the input for the Deblur algorithm. Each Deblurred sample is then checked for de novo chimeras, and the resulting sOTUs from all samples are combined into a single BIOM (20) table (with sequences labeled as the sOTU IDs). Download FIG S1, PDF file, 0.8 MB.
Copyright © 2017 Amir et al.
This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.
TABLE S1
The full error profile used by Deblur. Download TABLE S1, XLSX file, 0.03 MB.
Copyright © 2017 Amir et al.
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TABLE S2
Samples from the American Gut Project used for an integration, highlighting the sequencing round, sequencing location, and date of sequencing. Download TABLE S2, XLSX file, 0.1 MB.
Copyright © 2017 Amir et al.
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FIG S2
Howler monkey sOTUs identified by Deblur and DADA2. Reads from the howler monkey data set (18) are shown. (A and B) sOTUs identified per method (A) and their abundance (B). The fractions of overlapping sOTUs under conditions of increasing filtering based on minimum total read counts per sOTU are indicated. Download FIG S2, PDF file, 0.1 MB.
Copyright © 2017 Amir et al.
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FIG S3
Space characterization. Memory consumption on random subsets of one MiSeq run from reference 16 is indicated. (A) Memory use in kilobytes for Deblur, DADA2, and UNOISE2 over a log scale (B). Detail of memory use of Deblur in megabytes using a linear scale. Download FIG S3, PDF file, 0.9 MB.
Copyright © 2017 Amir et al.
This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.
